{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Section 2.3.2 Kurtosis",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPqq54jpEcVyFrWV5/kXy+Q",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "widgets": {
      "application/vnd.jupyter.widget-state+json": {
        "60be79cc3128450eb415228e04e9a847": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HBoxModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HBoxModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HBoxView",
            "box_style": "",
            "children": [
              "IPY_MODEL_210cd0a6b78342ee9e24cf5cc2bb54e4",
              "IPY_MODEL_aaf6ba3d242f4381a6e69f744f7fd57d",
              "IPY_MODEL_d42cfcf5f7eb48c3a8ae72e164ffc89f"
            ],
            "layout": "IPY_MODEL_a7ebcd77e28a4ac28362ddfb4c64069f"
          }
        },
        "210cd0a6b78342ee9e24cf5cc2bb54e4": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_3d4fac3d9df5425184250c76db2677a6",
            "placeholder": "​",
            "style": "IPY_MODEL_03835cbef7af4500aa55d85788d3a91f",
            "value": "100%"
          }
        },
        "aaf6ba3d242f4381a6e69f744f7fd57d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "FloatProgressModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "FloatProgressModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "ProgressView",
            "bar_style": "success",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_aa8b504d1bd24f109b62241a04ac05b3",
            "max": 95,
            "min": 0,
            "orientation": "horizontal",
            "style": "IPY_MODEL_c42d6288d0e44a19bc5c1ea8361df62d",
            "value": 95
          }
        },
        "d42cfcf5f7eb48c3a8ae72e164ffc89f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "HTMLModel",
          "model_module_version": "1.5.0",
          "state": {
            "_dom_classes": [],
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "HTMLModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/controls",
            "_view_module_version": "1.5.0",
            "_view_name": "HTMLView",
            "description": "",
            "description_tooltip": null,
            "layout": "IPY_MODEL_cbbf81c14ab54e7283a63bc675a9d7a5",
            "placeholder": "​",
            "style": "IPY_MODEL_28c2b76c351e4538b23bb1b3d06e72f6",
            "value": " 95/95 [00:08&lt;00:00, 11.57it/s]"
          }
        },
        "a7ebcd77e28a4ac28362ddfb4c64069f": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "3d4fac3d9df5425184250c76db2677a6": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "03835cbef7af4500aa55d85788d3a91f": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        },
        "aa8b504d1bd24f109b62241a04ac05b3": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "c42d6288d0e44a19bc5c1ea8361df62d": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "ProgressStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "ProgressStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "bar_color": null,
            "description_width": ""
          }
        },
        "cbbf81c14ab54e7283a63bc675a9d7a5": {
          "model_module": "@jupyter-widgets/base",
          "model_name": "LayoutModel",
          "model_module_version": "1.2.0",
          "state": {
            "_model_module": "@jupyter-widgets/base",
            "_model_module_version": "1.2.0",
            "_model_name": "LayoutModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "LayoutView",
            "align_content": null,
            "align_items": null,
            "align_self": null,
            "border": null,
            "bottom": null,
            "display": null,
            "flex": null,
            "flex_flow": null,
            "grid_area": null,
            "grid_auto_columns": null,
            "grid_auto_flow": null,
            "grid_auto_rows": null,
            "grid_column": null,
            "grid_gap": null,
            "grid_row": null,
            "grid_template_areas": null,
            "grid_template_columns": null,
            "grid_template_rows": null,
            "height": null,
            "justify_content": null,
            "justify_items": null,
            "left": null,
            "margin": null,
            "max_height": null,
            "max_width": null,
            "min_height": null,
            "min_width": null,
            "object_fit": null,
            "object_position": null,
            "order": null,
            "overflow": null,
            "overflow_x": null,
            "overflow_y": null,
            "padding": null,
            "right": null,
            "top": null,
            "visibility": null,
            "width": null
          }
        },
        "28c2b76c351e4538b23bb1b3d06e72f6": {
          "model_module": "@jupyter-widgets/controls",
          "model_name": "DescriptionStyleModel",
          "model_module_version": "1.5.0",
          "state": {
            "_model_module": "@jupyter-widgets/controls",
            "_model_module_version": "1.5.0",
            "_model_name": "DescriptionStyleModel",
            "_view_count": null,
            "_view_module": "@jupyter-widgets/base",
            "_view_module_version": "1.2.0",
            "_view_name": "StyleView",
            "description_width": ""
          }
        }
      }
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Farmhouse121/Adventures-in-Financial-Data-Science/blob/main/Book/Section%202.3%20The%20U.S.%20Stock%20Market%20Through%20Time/Section%202.3.2%20Kurtosis.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 535
        },
        "id": "IlEcdgpHdiTI",
        "outputId": "5a6d47c2-0397-422d-fd5a-b3f1804d46a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing yfinance and arch and getting the data...\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires requests~=2.23.0, but you have requests 2.27.1 which is incompatible.\n",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\u001b[0m\n",
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Open         High          Low        Close    Adj Close  \\\n",
              "Date                                                                          \n",
              "1928-01-04    17.719999    17.719999    17.719999    17.719999    17.719999   \n",
              "1928-01-05    17.549999    17.549999    17.549999    17.549999    17.549999   \n",
              "1928-01-06    17.660000    17.660000    17.660000    17.660000    17.660000   \n",
              "1928-01-09    17.500000    17.500000    17.500000    17.500000    17.500000   \n",
              "1928-01-10    17.370001    17.370001    17.370001    17.370001    17.370001   \n",
              "...                 ...          ...          ...          ...          ...   \n",
              "2022-04-04  4547.970215  4583.500000  4539.209961  4582.640137  4582.640137   \n",
              "2022-04-05  4572.450195  4593.450195  4514.169922  4525.120117  4525.120117   \n",
              "2022-04-06  4494.169922  4503.939941  4450.040039  4481.149902  4481.149902   \n",
              "2022-04-07  4474.649902  4521.160156  4450.299805  4500.209961  4500.209961   \n",
              "2022-04-08  4494.149902  4520.410156  4474.600098  4488.279785  4488.279785   \n",
              "\n",
              "                  Volume    Return  \n",
              "Date                                \n",
              "1928-01-04           NaN -0.225230  \n",
              "1928-01-05           NaN -0.959368  \n",
              "1928-01-06           NaN  0.626784  \n",
              "1928-01-09           NaN -0.906001  \n",
              "1928-01-10           NaN -0.742852  \n",
              "...                  ...       ...  \n",
              "2022-04-04  3.833500e+09  0.809094  \n",
              "2022-04-05  3.906230e+09 -1.255172  \n",
              "2022-04-06  4.137080e+09 -0.971692  \n",
              "2022-04-07  4.054010e+09  0.425339  \n",
              "2022-04-08  3.453040e+09 -0.265103  \n",
              "\n",
              "[23680 rows x 7 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b157925-7f24-4dc5-8d12-2801da2f5c8f\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Close</th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Volume</th>\n",
              "      <th>Return</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1928-01-04</th>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>17.719999</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.225230</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-05</th>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>17.549999</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.959368</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-06</th>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>17.660000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>0.626784</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-09</th>\n",
              "      <td>17.500000</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>17.500000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.906001</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1928-01-10</th>\n",
              "      <td>17.370001</td>\n",
              "      <td>17.370001</td>\n",
              "      <td>17.370001</td>\n",
              "      <td>17.370001</td>\n",
              "      <td>17.370001</td>\n",
              "      <td>NaN</td>\n",
              "      <td>-0.742852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-04</th>\n",
              "      <td>4547.970215</td>\n",
              "      <td>4583.500000</td>\n",
              "      <td>4539.209961</td>\n",
              "      <td>4582.640137</td>\n",
              "      <td>4582.640137</td>\n",
              "      <td>3.833500e+09</td>\n",
              "      <td>0.809094</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-05</th>\n",
              "      <td>4572.450195</td>\n",
              "      <td>4593.450195</td>\n",
              "      <td>4514.169922</td>\n",
              "      <td>4525.120117</td>\n",
              "      <td>4525.120117</td>\n",
              "      <td>3.906230e+09</td>\n",
              "      <td>-1.255172</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-06</th>\n",
              "      <td>4494.169922</td>\n",
              "      <td>4503.939941</td>\n",
              "      <td>4450.040039</td>\n",
              "      <td>4481.149902</td>\n",
              "      <td>4481.149902</td>\n",
              "      <td>4.137080e+09</td>\n",
              "      <td>-0.971692</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-07</th>\n",
              "      <td>4474.649902</td>\n",
              "      <td>4521.160156</td>\n",
              "      <td>4450.299805</td>\n",
              "      <td>4500.209961</td>\n",
              "      <td>4500.209961</td>\n",
              "      <td>4.054010e+09</td>\n",
              "      <td>0.425339</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022-04-08</th>\n",
              "      <td>4494.149902</td>\n",
              "      <td>4520.410156</td>\n",
              "      <td>4474.600098</td>\n",
              "      <td>4488.279785</td>\n",
              "      <td>4488.279785</td>\n",
              "      <td>3.453040e+09</td>\n",
              "      <td>-0.265103</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>23680 rows × 7 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b157925-7f24-4dc5-8d12-2801da2f5c8f')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-3b157925-7f24-4dc5-8d12-2801da2f5c8f button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3b157925-7f24-4dc5-8d12-2801da2f5c8f');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 1
        }
      ],
      "source": [
        "print(\"Installing yfinance and arch and getting the data...\")\n",
        "!pip install arch 1>/dev/null\n",
        "!pip install yfinance 1>/dev/null\n",
        "from yfinance import download\n",
        "import pandas as pd\n",
        "import numpy as np ;\n",
        "import matplotlib.pyplot as pl\n",
        "from statsmodels.base.model import GenericLikelihoodModel\n",
        "from datetime import datetime\n",
        "zero,one,two,three,five,ten,hundred=0e0,1e0,2e0,3e0,5e0,1e1,1e2 # some friendly numbers\n",
        "half,GoldenRatio=one/two,(one+np.sqrt(five))/two\n",
        "\n",
        "# get the daily returns of the S&P 500 \n",
        "SPX=download('^GSPC','1928-01-03').dropna()\n",
        "SPX['Return']=SPX['Adj Close'].pct_change()*hundred\n",
        "SPX.index=pd.DatetimeIndex(SPX.index).to_period('D')\n",
        "SPX.dropna(inplace=True)\n",
        "SPX.loc[SPX[\"Volume\"]==0,\"Volume\"]=np.nan\n",
        "SPX"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# fit a GARCH model using arch package\n",
        "print(\"Installing ARCH and fitting model with GED innovations.\")\n",
        "from arch.univariate import ARX, GARCH, GeneralizedError\n",
        "from tqdm.notebook import tqdm\n",
        "results=pd.DataFrame({\"year\":[],\"mu\":[],\"phi\":[],\"C\":[],\"A\":[],\"B\":[],\"kappa\":[]}).set_index(\"year\")\n",
        "results.index=pd.DatetimeIndex(results.index).to_period('Y')\n",
        "\n",
        "for year in tqdm(range(SPX.index[0].year,SPX.index[-1].year+1)):\n",
        "    model = ARX(SPX.loc[SPX.index.year==year,\"Return\"],lags=[1])\n",
        "    model.volatility = GARCH(1, 0, 1)\n",
        "    model.distribution = GeneralizedError()\n",
        "    fit=model.fit(update_freq=0,disp='off')\n",
        "\n",
        "    if not fit.convergence_flag:\n",
        "        mu,phi,C,A,B,nu=tuple(fit.params)\n",
        "        results=results.append(pd.DataFrame({\"year\":[year],\"mu\":[mu],\"phi\":[phi],\"C\":[C],\"A\":[A],\"B\":[B],\"kappa\":[nu/two]}).set_index(\"year\"))\n",
        "\n",
        "results"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 601,
          "referenced_widgets": [
            "60be79cc3128450eb415228e04e9a847",
            "210cd0a6b78342ee9e24cf5cc2bb54e4",
            "aaf6ba3d242f4381a6e69f744f7fd57d",
            "d42cfcf5f7eb48c3a8ae72e164ffc89f",
            "a7ebcd77e28a4ac28362ddfb4c64069f",
            "3d4fac3d9df5425184250c76db2677a6",
            "03835cbef7af4500aa55d85788d3a91f",
            "aa8b504d1bd24f109b62241a04ac05b3",
            "c42d6288d0e44a19bc5c1ea8361df62d",
            "cbbf81c14ab54e7283a63bc675a9d7a5",
            "28c2b76c351e4538b23bb1b3d06e72f6"
          ]
        },
        "id": "wNqwBZ61d0v7",
        "outputId": "eafbd9a8-d8f7-43ff-8072-7b05181acc6c"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Installing ARCH and fitting model with GED innovations.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "  0%|          | 0/95 [00:00<?, ?it/s]"
            ],
            "application/vnd.jupyter.widget-view+json": {
              "version_major": 2,
              "version_minor": 0,
              "model_id": "60be79cc3128450eb415228e04e9a847"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/arch/univariate/distribution.py:1192: RuntimeWarning: overflow encountered in power\n",
            "  lls -= 0.5 * abs(resids / (sqrt(sigma2) * c)) ** nu\n",
            "/usr/local/lib/python3.7/dist-packages/arch/univariate/distribution.py:1192: RuntimeWarning: overflow encountered in power\n",
            "  lls -= 0.5 * abs(resids / (sqrt(sigma2) * c)) ** nu\n",
            "/usr/local/lib/python3.7/dist-packages/arch/univariate/distribution.py:1192: RuntimeWarning: overflow encountered in power\n",
            "  lls -= 0.5 * abs(resids / (sqrt(sigma2) * c)) ** nu\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            mu       phi         C             A         B     kappa\n",
              "year                                                                \n",
              "1928  0.213503  0.045765  0.128572  1.019873e-01  0.750586  0.674925\n",
              "1929  0.216339 -0.053070  0.174688  2.493820e-01  0.724788  0.569474\n",
              "1930  0.112767 -0.106637  0.126813  2.632717e-01  0.730457  0.836508\n",
              "1931 -0.326928 -0.132279  0.318704  9.778081e-02  0.852799  0.664308\n",
              "1932 -0.126065  0.023032  1.451915  1.129598e-01  0.757731  0.795575\n",
              "...        ...       ...       ...           ...       ...       ...\n",
              "2018  0.070139 -0.025451  0.033166  2.007801e-01  0.799220  0.584140\n",
              "2019  0.134195 -0.054537  0.041216  2.173376e-01  0.717367  0.672854\n",
              "2020  0.251211 -0.217351  0.088885  3.011224e-01  0.698878  0.632700\n",
              "2021  0.131095 -0.022648  0.146024  2.848040e-01  0.520630  0.681681\n",
              "2022 -0.052857  0.104787  0.123903  1.324797e-13  0.931935  1.985266\n",
              "\n",
              "[95 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-589ed71b-244f-4fd4-88d8-eb2a21ed8a53\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>mu</th>\n",
              "      <th>phi</th>\n",
              "      <th>C</th>\n",
              "      <th>A</th>\n",
              "      <th>B</th>\n",
              "      <th>kappa</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>year</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1928</th>\n",
              "      <td>0.213503</td>\n",
              "      <td>0.045765</td>\n",
              "      <td>0.128572</td>\n",
              "      <td>1.019873e-01</td>\n",
              "      <td>0.750586</td>\n",
              "      <td>0.674925</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1929</th>\n",
              "      <td>0.216339</td>\n",
              "      <td>-0.053070</td>\n",
              "      <td>0.174688</td>\n",
              "      <td>2.493820e-01</td>\n",
              "      <td>0.724788</td>\n",
              "      <td>0.569474</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1930</th>\n",
              "      <td>0.112767</td>\n",
              "      <td>-0.106637</td>\n",
              "      <td>0.126813</td>\n",
              "      <td>2.632717e-01</td>\n",
              "      <td>0.730457</td>\n",
              "      <td>0.836508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1931</th>\n",
              "      <td>-0.326928</td>\n",
              "      <td>-0.132279</td>\n",
              "      <td>0.318704</td>\n",
              "      <td>9.778081e-02</td>\n",
              "      <td>0.852799</td>\n",
              "      <td>0.664308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1932</th>\n",
              "      <td>-0.126065</td>\n",
              "      <td>0.023032</td>\n",
              "      <td>1.451915</td>\n",
              "      <td>1.129598e-01</td>\n",
              "      <td>0.757731</td>\n",
              "      <td>0.795575</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2018</th>\n",
              "      <td>0.070139</td>\n",
              "      <td>-0.025451</td>\n",
              "      <td>0.033166</td>\n",
              "      <td>2.007801e-01</td>\n",
              "      <td>0.799220</td>\n",
              "      <td>0.584140</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2019</th>\n",
              "      <td>0.134195</td>\n",
              "      <td>-0.054537</td>\n",
              "      <td>0.041216</td>\n",
              "      <td>2.173376e-01</td>\n",
              "      <td>0.717367</td>\n",
              "      <td>0.672854</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2020</th>\n",
              "      <td>0.251211</td>\n",
              "      <td>-0.217351</td>\n",
              "      <td>0.088885</td>\n",
              "      <td>3.011224e-01</td>\n",
              "      <td>0.698878</td>\n",
              "      <td>0.632700</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2021</th>\n",
              "      <td>0.131095</td>\n",
              "      <td>-0.022648</td>\n",
              "      <td>0.146024</td>\n",
              "      <td>2.848040e-01</td>\n",
              "      <td>0.520630</td>\n",
              "      <td>0.681681</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2022</th>\n",
              "      <td>-0.052857</td>\n",
              "      <td>0.104787</td>\n",
              "      <td>0.123903</td>\n",
              "      <td>1.324797e-13</td>\n",
              "      <td>0.931935</td>\n",
              "      <td>1.985266</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>95 rows × 6 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-589ed71b-244f-4fd4-88d8-eb2a21ed8a53')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-589ed71b-244f-4fd4-88d8-eb2a21ed8a53 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-589ed71b-244f-4fd4-88d8-eb2a21ed8a53');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Figure 2.12\n",
        "figure,plot=pl.subplots(figsize=(ten*GoldenRatio,ten))\n",
        "figure.suptitle(\"\\nEstimated Kurtosis Parameter of Daily Returns for S&P 500\",fontsize=22)\n",
        "plot.set_title(\"%s to %s\" % (min(SPX.index),max(SPX.index)),fontsize=18)\n",
        "plot.plot(results.index,results[\"kappa\"],'-',color=\"red\",label=\"$\\\\hat{\\\\phi}$\")\n",
        "\n",
        "plot.axhline(half,color='black',lw=1)\n",
        "#fisher_error=one/(np.sqrt(252)-three)\n",
        "#plot.axhspan(-fisher_error,fisher_error,color='black',alpha=0.2)\n",
        "#plot.axhspan(-two*fisher_error,two*fisher_error,color='black',alpha=0.2)\n",
        "#plot.axhline(-two*fisher_error,color='black',linestyle=':',linewidth=1)\n",
        "#plot.axhline(+two*fisher_error,color='black',linestyle=':',linewidth=1)\n",
        "\n",
        "plot.set_ylabel(\"Estimated $\\\\kappa$ Parameter\",fontsize=14)\n",
        "plot.set_ylim(zero,1.5);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 663
        },
        "id": "gVYb5plNuw1X",
        "outputId": "6aa25d68-388f-46c9-bd9e-fdeadfe21bfb"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1164.98x720 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "7n3YUbsfwLqV"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}